Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations543
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.2 KiB
Average record size in memory72.0 B

Variable types

Numeric8

Alerts

Battery_Power is highly overall correlated with Mobile_SizeHigh correlation
Mobile_Size is highly overall correlated with Battery_Power and 2 other fieldsHigh correlation
Price is highly overall correlated with Mobile_Size and 1 other fieldsHigh correlation
Ratings is highly overall correlated with Mobile_Size and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-12-15 07:06:54.389158
Analysis finished2024-12-15 07:06:57.334665
Duration2.95 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Ratings
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.067035
Minimum2.8
Maximum4.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:57.368426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.4
Q13.8
median4.1
Q34.4
95-th percentile4.6
Maximum4.8
Range2
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.36396729
Coefficient of variation (CV)0.089492048
Kurtosis-0.27952162
Mean4.067035
Median Absolute Deviation (MAD)0.3
Skewness-0.42820126
Sum2208.4
Variance0.13247219
MonotonicityNot monotonic
2024-12-15T12:36:57.416481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4.1 69
12.7%
4.5 62
11.4%
3.9 54
9.9%
4.4 53
9.8%
3.8 48
8.8%
4.3 45
8.3%
4.2 44
8.1%
4 34
6.3%
3.5 26
 
4.8%
3.7 26
 
4.8%
Other values (10) 82
15.1%
ValueCountFrequency (%)
2.8 1
 
0.2%
3 4
 
0.7%
3.1 1
 
0.2%
3.2 2
 
0.4%
3.3 2
 
0.4%
3.4 20
3.7%
3.5 26
4.8%
3.6 21
3.9%
3.7 26
4.8%
3.8 48
8.8%
ValueCountFrequency (%)
4.8 1
 
0.2%
4.7 15
 
2.8%
4.6 15
 
2.8%
4.5 62
11.4%
4.4 53
9.8%
4.3 45
8.3%
4.2 44
8.1%
4.1 69
12.7%
4 34
6.3%
3.9 54
9.9%

RAM
Real number (ℝ)

Distinct10
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9208103
Minimum0
Maximum12
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:57.464302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median6
Q36
95-th percentile8
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0642783
Coefficient of variation (CV)0.34864794
Kurtosis1.9777834
Mean5.9208103
Median Absolute Deviation (MAD)0
Skewness0.42735181
Sum3215
Variance4.2612451
MonotonicityNot monotonic
2024-12-15T12:36:57.509824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 309
56.9%
8 81
 
14.9%
4 69
 
12.7%
3 27
 
5.0%
12 22
 
4.1%
2 15
 
2.8%
1 12
 
2.2%
10 4
 
0.7%
5 2
 
0.4%
0 2
 
0.4%
ValueCountFrequency (%)
0 2
 
0.4%
1 12
 
2.2%
2 15
 
2.8%
3 27
 
5.0%
4 69
 
12.7%
5 2
 
0.4%
6 309
56.9%
8 81
 
14.9%
10 4
 
0.7%
12 22
 
4.1%
ValueCountFrequency (%)
12 22
 
4.1%
10 4
 
0.7%
8 81
 
14.9%
6 309
56.9%
5 2
 
0.4%
4 69
 
12.7%
3 27
 
5.0%
2 15
 
2.8%
1 12
 
2.2%
0 2
 
0.4%

ROM
Real number (ℝ)

Distinct20
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.441989
Minimum2
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:57.555413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16
Q132
median32
Q364
95-th percentile128
Maximum256
Range254
Interquartile range (IQR)32

Descriptive statistics

Standard deviation48.788608
Coefficient of variation (CV)0.83482114
Kurtosis6.1695776
Mean58.441989
Median Absolute Deviation (MAD)16
Skewness2.300239
Sum31734
Variance2380.3283
MonotonicityNot monotonic
2024-12-15T12:36:57.602421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
32 235
43.3%
64 153
28.2%
128 69
 
12.7%
256 17
 
3.1%
16 17
 
3.1%
4 14
 
2.6%
24 10
 
1.8%
25 6
 
1.1%
20 3
 
0.6%
12 3
 
0.6%
Other values (10) 16
 
2.9%
ValueCountFrequency (%)
2 1
 
0.2%
3 2
 
0.4%
4 14
2.6%
8 2
 
0.4%
10 1
 
0.2%
12 3
 
0.6%
15 1
 
0.2%
16 17
3.1%
20 3
 
0.6%
22 1
 
0.2%
ValueCountFrequency (%)
256 17
 
3.1%
128 69
 
12.7%
64 153
28.2%
56 2
 
0.4%
51 1
 
0.2%
40 3
 
0.6%
35 2
 
0.4%
32 235
43.3%
25 6
 
1.1%
24 10
 
1.8%

Mobile_Size
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6020866
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:57.652370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.4
Q14.5
median4.58
Q36.2
95-th percentile6.599
Maximum44
Range42
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation4.4848259
Coefficient of variation (CV)0.80056347
Kurtosis67.161484
Mean5.6020866
Median Absolute Deviation (MAD)0.18
Skewness8.1219462
Sum3041.933
Variance20.113663
MonotonicityNot monotonic
2024-12-15T12:36:57.703488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4.5 128
23.6%
4.54 106
19.5%
4.77 42
 
7.7%
6.5 20
 
3.7%
4.7 17
 
3.1%
4.4 13
 
2.4%
3.7 13
 
2.4%
6.3 13
 
2.4%
6.2 12
 
2.2%
4.58 11
 
2.0%
Other values (37) 168
30.9%
ValueCountFrequency (%)
2 6
 
1.1%
3.7 13
 
2.4%
4.4 13
 
2.4%
4.5 128
23.6%
4.503 1
 
0.2%
4.52 2
 
0.4%
4.54 106
19.5%
4.57 2
 
0.4%
4.58 11
 
2.0%
4.7 17
 
3.1%
ValueCountFrequency (%)
44 7
 
1.3%
7 1
 
0.2%
6.7 9
1.7%
6.67 8
 
1.5%
6.6 3
 
0.6%
6.59 3
 
0.6%
6.55 2
 
0.4%
6.53 7
 
1.3%
6.52 1
 
0.2%
6.5 20
3.7%

Primary_Cam
Real number (ℝ)

Distinct11
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.883978
Minimum5
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:57.743238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q148
median48
Q348
95-th percentile64
Maximum64
Range59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.952909
Coefficient of variation (CV)0.2287385
Kurtosis1.8773801
Mean47.883978
Median Absolute Deviation (MAD)0
Skewness-0.68341888
Sum26001
Variance119.96622
MonotonicityNot monotonic
2024-12-15T12:36:57.781737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
48 311
57.3%
64 111
 
20.4%
38 49
 
9.0%
35 44
 
8.1%
25 7
 
1.3%
8 5
 
0.9%
20 4
 
0.7%
16 4
 
0.7%
40 3
 
0.6%
26 3
 
0.6%
ValueCountFrequency (%)
5 2
 
0.4%
8 5
 
0.9%
16 4
 
0.7%
20 4
 
0.7%
25 7
 
1.3%
26 3
 
0.6%
35 44
 
8.1%
38 49
 
9.0%
40 3
 
0.6%
48 311
57.3%
ValueCountFrequency (%)
64 111
 
20.4%
48 311
57.3%
40 3
 
0.6%
38 49
 
9.0%
35 44
 
8.1%
26 3
 
0.6%
25 7
 
1.3%
20 4
 
0.7%
16 4
 
0.7%
8 5
 
0.9%

Selfi_Cam
Real number (ℝ)

Distinct21
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0773481
Minimum0
Maximum23
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:57.822567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median8
Q312
95-th percentile20
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7457014
Coefficient of variation (CV)0.52280703
Kurtosis0.77652718
Mean9.0773481
Median Absolute Deviation (MAD)3
Skewness0.83091436
Sum4929
Variance22.521682
MonotonicityNot monotonic
2024-12-15T12:36:57.864658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
8 239
44.0%
5 72
 
13.3%
12 52
 
9.6%
2 42
 
7.7%
13 34
 
6.3%
20 21
 
3.9%
15 20
 
3.7%
1 10
 
1.8%
16 8
 
1.5%
11 8
 
1.5%
Other values (11) 37
 
6.8%
ValueCountFrequency (%)
0 3
 
0.6%
1 10
 
1.8%
2 42
 
7.7%
4 1
 
0.2%
5 72
 
13.3%
6 1
 
0.2%
7 4
 
0.7%
8 239
44.0%
10 2
 
0.4%
11 8
 
1.5%
ValueCountFrequency (%)
23 6
 
1.1%
22 7
 
1.3%
21 2
 
0.4%
20 21
3.9%
18 3
 
0.6%
17 1
 
0.2%
16 8
 
1.5%
15 20
3.7%
14 7
 
1.3%
13 34
6.3%

Battery_Power
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3211.8969
Minimum1020
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:57.912046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1200
Q13000
median3000
Q33800
95-th percentile4772
Maximum6000
Range4980
Interquartile range (IQR)800

Descriptive statistics

Standard deviation910.52517
Coefficient of variation (CV)0.28348518
Kurtosis0.51639861
Mean3211.8969
Median Absolute Deviation (MAD)500
Skewness-0.22755241
Sum1744060
Variance829056.08
MonotonicityNot monotonic
2024-12-15T12:36:57.966023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3000 189
34.8%
3500 71
 
13.1%
3800 57
 
10.5%
2500 28
 
5.2%
4700 23
 
4.2%
5000 23
 
4.2%
1750 21
 
3.9%
2800 15
 
2.8%
1200 13
 
2.4%
1050 12
 
2.2%
Other values (39) 91
16.8%
ValueCountFrequency (%)
1020 3
 
0.6%
1050 12
2.2%
1080 1
 
0.2%
1100 1
 
0.2%
1200 13
2.4%
1500 2
 
0.4%
1550 5
 
0.9%
1750 21
3.9%
1900 4
 
0.7%
2000 5
 
0.9%
ValueCountFrequency (%)
6000 2
 
0.4%
5000 23
4.2%
4900 1
 
0.2%
4780 2
 
0.4%
4700 23
4.2%
4600 1
 
0.2%
4550 1
 
0.2%
4440 3
 
0.6%
4300 1
 
0.2%
4230 8
 
1.5%

Price
Real number (ℝ)

High correlation 

Distinct241
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11799.173
Minimum479
Maximum153000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-12-15T12:36:58.017147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum479
5-th percentile649
Q1970
median1499
Q315999
95-th percentile47896.1
Maximum153000
Range152521
Interquartile range (IQR)15029

Descriptive statistics

Standard deviation21779.492
Coefficient of variation (CV)1.845849
Kurtosis14.917865
Mean11799.173
Median Absolute Deviation (MAD)800
Skewness3.5016617
Sum6406951
Variance4.7434626 × 108
MonotonicityNot monotonic
2024-12-15T12:36:58.073000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
649 17
 
3.1%
1199 15
 
2.8%
1099 14
 
2.6%
799 13
 
2.4%
1299 10
 
1.8%
899 9
 
1.7%
599 9
 
1.7%
3999 8
 
1.5%
1349 8
 
1.5%
1799 7
 
1.3%
Other values (231) 433
79.7%
ValueCountFrequency (%)
479 1
 
0.2%
539 1
 
0.2%
559 1
 
0.2%
595 1
 
0.2%
599 9
1.7%
629 3
 
0.6%
639 6
 
1.1%
645 1
 
0.2%
649 17
3.1%
650 2
 
0.4%
ValueCountFrequency (%)
153000 1
 
0.2%
140300 3
0.6%
121300 2
0.4%
117100 2
0.4%
112450 1
 
0.2%
106600 1
 
0.2%
103000 1
 
0.2%
84900 1
 
0.2%
77999 1
 
0.2%
75000 1
 
0.2%

Interactions

2024-12-15T12:36:56.909813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.536073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.009686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.302609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.617789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.921104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.299709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.594824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.953350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.659735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.050328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.343425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.661087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.962115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.339659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.640326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.992469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.750150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.086497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.378739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.701415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.998276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.374547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.683128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:57.030866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.814027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.121718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.415350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.742625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.119350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.408556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.720015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:57.067501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.851581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.156298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.449291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.776736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.153478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.442228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.756508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:57.106313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.889821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.191694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.485671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.811772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.188859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.477469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.795281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:57.143010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.927229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.226078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.518596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.845864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.222583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.511720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.831453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:57.182186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:54.965953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.261870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.569607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:55.881377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.258999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.550833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-15T12:36:56.869266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-15T12:36:58.113408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Battery_PowerMobile_SizePricePrimary_CamRAMROMRatingsSelfi_Cam
Battery_Power1.0000.5110.495-0.164-0.0280.3910.461-0.145
Mobile_Size0.5111.0000.751-0.3420.0110.4810.601-0.268
Price0.4950.7511.000-0.3250.0110.4460.684-0.171
Primary_Cam-0.164-0.342-0.3251.0000.138-0.146-0.2170.226
RAM-0.0280.0110.0110.1381.0000.3090.0750.221
ROM0.3910.4810.446-0.1460.3091.0000.342-0.052
Ratings0.4610.6010.684-0.2170.0750.3421.000-0.127
Selfi_Cam-0.145-0.268-0.1710.2260.221-0.052-0.1271.000

Missing values

2024-12-15T12:36:57.237849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-15T12:36:57.293629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RatingsRAMROMMobile_SizePrimary_CamSelfi_CamBattery_PowerPrice
04.34.0128.06.004813.0400024999
13.46.064.04.504812.0400015999
24.34.04.04.506416.0400015000
34.46.064.06.404815.0380018999
44.56.0128.06.183515.0380018999
54.78.0128.05.803512.05000140300
64.48.0128.06.70645.0470029999
74.58.0128.06.704812.0470047999
84.44.0128.06.53482.0402016490
94.58.0256.06.18355.0380022999
RatingsRAMROMMobile_SizePrimary_CamSelfi_CamBattery_PowerPrice
7644.56.0128.06.394813.0403024999
7744.14.040.04.77488.030001080
7753.84.064.04.544813.012003580
7764.06.032.04.546411.025001695
7773.56.032.04.52488.035002599
7814.18.064.04.54648.025001390
7953.84.064.04.544815.012003580
7964.14.040.04.774815.030001080
8023.86.032.04.544812.028001299
8063.56.032.04.506415.01050799